There was a time where data collection was onerous and assessing the data was manual. This meant that organizations had to strike a critical balance between the speed and quality of their decision-making. We’ve always made decisions based on data; the process was slow, labor intensive, and required focus on a limited set of variables. There can be no denying that technology has changed the speed and scale of DDDM. With the capabilities present today in elastic cloud computing, big data, AI, and the Internet of Things, our ability to collect, process, and analyze data to make decisions has become exponentially faster and more wide-reaching. This automatically means better outcomes, right?

Consider the words of Joy Buolamwini, the MIT student who uncovered algorithmic bias in facial recognition programs and is a central figure in the Netflix documentary, Coded Bias. Buolamwini says, “If you are thinking about data and artificial intelligence, in many ways data is destiny. It is what we are using to teach machines different kinds of patterns. If you have largely skewed datasets that are being used to train these systems, you can also have skewed results.”  She said this in the context of racial bias in facial recognition programs, but the exact same principle can be applied to any AI model in a business or industrial setting.